Content-Consumption-Aware Resource Allocation for Server Continuity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing load balancing methods in cloud computing fail to consider user behavior habits, leading to uneven server load distribution and potential service interruptions due to long-term occupation of resources by user terminals, especially in audio/video services.
Innovation Solution
A resource allocation method that assesses historical content consumption time lengths and server load conditions to dynamically distribute traffic flow requests, optimizing server allocation by identifying servers with the least load and considering user behavior habits to avoid service interruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If round robin or random method is used to map user requests to servers, then traffic flow distribution is simplified, but server load becomes uneven and service interruption occurs
Solution Approach 1:
The patent applies dynamics by transitioning from static load balancing methods (round robin, random) to a dynamic assessment mechanism that continuously monitors historical content consumption time lengths and server load conditions. The load balancer dynamically adjusts traffic routing decisions based on real-time data, ensuring optimal resource allocation while preventing service interruptions through adaptive response to changing system states.
Solution Approach 2:
The patent implements feedback by having the load balancer assess historical content consumption time lengths from user terminals and use this information to determine current server load conditions. This feedback loop enables the system to learn from past behavior patterns and adjust traffic distribution accordingly, resolving the contradiction between simple routing and reliable service continuity.
2Ease of operation
If user terminals occupy servers for long periods, then user experience is improved, but server load increases excessively and causes service interruption
Solution Approach 1:
The patent applies preliminary action by having the load balancer assess historical content consumption time lengths before routing new traffic flow requests. This proactive evaluation allows the system to predict potential server overload conditions and distribute traffic in advance to prevent excessive load accumulation, while still allowing long-term occupation when necessary for user experience.
Solution Approach 2:
The patent changes the parameter used for load balancing from simple request count to historical content consumption time length. By considering the duration of content consumption patterns, the system can better allocate servers to users who need long-term service while preventing any single server from becoming overloaded, thus balancing user experience with server load management.
3Device complexity
If servers are allocated without considering historical consumption data, then resource allocation is simplified, but resource utilization efficiency decreases
Solution Approach 1:
The patent applies self-service by enabling the load balancer to automatically assess historical content consumption time lengths and determine server load conditions without manual intervention. The system self-adjusts traffic distribution based on the analyzed data, improving resource utilization efficiency while maintaining relatively simple operation through automated decision-making.
Data Source
AI summary
An apparatus, computer-readable memory storing a program, and method for resource allocation, including receiving a traffic flow request packet from a first terminal, the packet comprising one or more pieces of historical content traffic flow consumption time length information; determining a content traffic flow consumption time length feature of the first terminal based on the one or more pieces of historical content traffic flow consumption time length information; obtaining a load quantity of one or more servers based on the content traffic flow consumption time length feature meeting a historical content traffic flow consumption time length condition, each second terminal having a content traffic flow consumption time length greater than a first traffic flow time length threshold; determining a server with the least load quantity among the one or more servers as a first server; and transmitting the traffic flow request packet to the first server.